Recent advancements in large language models (LLMs) have highlighted their potential across a variety of tasks, but their performance still heavily relies on the design of effective prompts.Existing methods for automatic prompt optimization face two challenges: lack of diversity, limiting the exploration of valuable and innovative directions and semantic drift, where optimizations for one task can degrade performance in others.To address these issues, we propose Residual Optimization Tree (RIOT), a novel framework for automatic prompt optimization.RIOT iteratively refines prompts through text gradients, generating multiple semantically diverse candidates at each step, and selects the best prompt using perplexity.Additionally, RIOT incorporates the text residual connection to mitigate semantic drift by selectively retaining beneficial content across optimization iterations.A tree structure efficiently manages the optimization process, ensuring scalability and flexibility.Extensive experiments across five benchmarks -covering commonsense, mathematical, logical, temporal, and semantic reasoning -demonstrate that RIOT outperforms both previous prompt optimization methods and manual prompting.

In recent years, with the rapid development of automated driving technology, the safety and quality of automated driving software have become important concerns.Prior studies have shown that code clones are prevalent in autonomous driving software and significantly impact software quality, maintenance costs, and module independence.Although code cloning can improve development productivity, they are more likely to introduce co-modifications and software defects, which leads to high detection and fixing costs.In this paper, we propose a novel approach to predict defective code clones in autonomous driving software by focusing specifically on clone code snippets as prediction targets.We designed a comprehensive set of nine code metrics characterizing clone snippets from three dimensions: code size, complexity, and historical modifications.Using six machine learning algorithms, we built prediction models and evaluated them on two representative L4 opensource autonomous driving platforms: Apollo and Autoware.Our experimental results demonstrate that the developed prediction models achieve good performance with accuracy rates ranging from 82.7% to 94.4%.Through principal component analysis, we identified that the number of added lines, modification frequency, and clone line count are the most significant factors contributing to defect proneness in code clones.We believe that this work enables developers to locate defective code clones more precisely.Thus, they can improve testing efficiency, reduce bug fix costs, and ultimately improve the quality and reliability of autonomous driving software.

Cancer’s inherent heterogeneity demands a multimodal approach to provide an accurate prognosis, taking into account histological, clinical, and genomic data. As the field of artificial intelligence evolves with advancements in multimodal learning, its role in survival analysis becomes increasingly critical. We introduce the Multimodal Survival Ensemble Network (MSEN), a novel weakly-supervised framework designed for the seamless integration of genomic data and histopathological images. Not only does our method preserve the heterogeneity among different genomic modalities during integration, but it also ensures superior retention of spatial information in histopathological images compared to traditional techniques. Rigorous evaluations across five datasets highlight MSEN’s superior performance, marking a progressive step in cancer prognosis.

Anomaly detection has emerged as a prominent research area with extensive exploration across various applications. Existing methods predominantly focus on detecting all anomalies exhibiting unusual patterns, however, they overlook the critical need to prioritize the detection of target anomaly categories (anomalies of primary interest) that could pose significant threats to various systems. This oversight results in the excessive involvement of valuable human labor and resources in dealing with non-target anomalies (that are of lower interest). This work is focused on target-class anomaly detection, which entails overcoming several challenges: (1) deficient prior information regarding non-target anomalies and (2) an elevated false positive rate caused by the presence of non-target anomalies. Thus, we introduce a novel semi-supervised model, called TargAD, which leverages a few labeled target anomalies, along with potential non-target anomaly candidates and normal candidates selected from unlabeled data. By introducing a novel loss function, TargAD effectively maximizes the distributional differences among normal candidates, target anomalies, and non-target anomaly candidates, leading to a significant improvement in detecting target anomalies. Furthermore, when confronted with novel non-target anomaly scenarios, TargAD maintains its accuracy in detecting target anomalies. We conducted extensive experiments, the results of which demonstrate that TargAD outperforms eleven state-of-the-art baselines on a real-world dataset and three publicly available datasets, with average AUPRC improvements of 5.9%-24.8%, 9.2%-57.8%, 2.7%-71.3%, and 2.0%-70.3%, respectively.

Adopting Graph Convolutional Networks (GCNs) for transductive node classification is a hot research direction in artificial intelligence . Vanilla GCNs are primarily under-confident and struggle to clarify the final classification results explicitly due to the lack of supervision. Existing works mainly alleviated this issue by improving annotation deficiency and introducing addition regularization terms. However, these methods need to re-train the model from the beginning, which is computationally expensive for large dataset and model. To deal with this problem, a novel confidence correction mechanism (CCM) for trained GCNs is proposed in this work. Such mechanism aims at calibrating the confidence output of each node in the inference stage by jointly inferring the feature and predicted pseudo label. Specifically, in the inference stage, it uses the predicted pseudo label to select target-related features over all network to obtain a more confident and better result. Such selectivity is formulated as an optimization problem to maximize the category score of each node. In addition, the greedy optimization strategy is utilized to solve this problem and we have mathematically proven that the proposed mechanism can reach the local optimum by mathematical induction . Note that such mechanism is flexible and can be introduced to most GCN-based model. Extensive experimental results on benchmark datasets show that the proposed method can promote the confidence of the final target category and improve the performance of GCNs in the inference stage.

Emerging Internet of Things (IoT) applications, such as cashier-less shopping, mobile ads targeting, and geo-based augmented reality (AR), are expected to bring us much more convenience and infotainment. To realize this amazing future, we need to feed these applications with user locations of (sub)meter-level resolution anytime and anywhere. Unfortunately, many widely used location sources are either unavailable indoor (e.g., global positioning system) or coarse grained (e.g., user check-ins). In order to provide ubiquitous localization services, the widespread WiFi signals are being leveraged to establish (sub)meter-level localization systems. Fine-grained WiFi propagation characteristics, which are sensitive to human body locations, have been employed to create location fingerprints. However, these WiFi characteristics are also sensitive to: 1) the body shapes of different users and 2) the objects in the background environment. Consequently, systems based on WiFi fingerprints are vulnerable in the presence of: 1) new users with different body shapes and 2) daily changes of the environment, e.g., opening/closing doors. To tackle this issue, this article proposes a WiFi-based localization system based on domain-adaptation with cluster assumption, named Fidora. Fidora is able to: 1) localize different users with labeled data from only one or two example users and 2) localize the same user in a changed environment without labeling any new data. To achieve these, Fidora integrates two major modules. It first adopts a data augmenter that introduces data diversity using a variational autoencoder (VAE). It then trains a domain-adaptive classifier that adjusts itself to newly collected unlabeled data using a joint classification-reconstruction structure. We conducted real-world experiments to evaluate Fidora against the state of the art. It is demonstrated that when tested on an unlabeled user, Fidora increases the average$F1$score by 17.8% and improves the worst case accuracy by 20.2%. Moreover, when applied in a varied environment, Fidora outperforms the state of the art by 23.1%.

To fully support the emerging location-aware applications, location information with meter-level resolution (or even higher) is required anytime and anywhere. Unfortunately, most of the current location sources (e.g., GPS and check-in data) either are unavailable indoor or provide only house-level resolutions. To fill the gap, this paper utilizes the ubiquitous WiFi signals to establish a (sub)meter-level localization system, which employs WiFi propagation characteristics as location fingerprints. However, an unsolved issue of these WiFi fingerprints lies in their inconsistency across different users. In other words, WiFi fingerprints collected from one user may not be used to localize another user. To address this issue, we propose a WiFi-based Domain-adaptive system FiDo, which is able to localize many different users with labelled data from only one or two example users. FiDo contains two modules: 1) a data augmenter that introduces data diversity using a Variational Autoencoder (VAE); and 2) a domain-adaptive classifier that adjusts itself to newly collected unlabelled data using a joint classification-reconstruction structure. Compared to the state of the art, FiDo increases average F1 score by 11.8% and improves the worst-case accuracy by 20.2%.